In [1]:
import numpy             as np
import pandas            as pd
import scipy.stats       as stats
import seaborn           as sns
import matplotlib.pyplot as plt
import plotly.express    as px

%matplotlib inline

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC, SVR
from sklearn import model_selection
from sklearn.metrics import precision_recall_fscore_support
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import StratifiedKFold, cross_val_score, KFold, LeavePOut
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, LeaveOneOut, ShuffleSplit
from sklearn.ensemble import RandomForestClassifier, VotingClassifier, BaggingClassifier
from sklearn import preprocessing
from sklearn import metrics
from sklearn.metrics import mean_squared_error
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.neural_network import MLPClassifier

from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import RandomOverSampler, SMOTENC, SMOTE, ADASYN

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import RobustScaler

from scipy.stats import zscore
from scipy.stats import randint as sp_randint
from scipy.stats import uniform as sp_uniform

Steps and tasks: [ Total Score: 60 points]¶

  1. Import and understand the data. [5 Marks]

A. Import ‘signal-data.csv’ as DataFrame. [2 Marks]

In [2]:
df_signal_data = pd.read_csv('signal-data.csv')
In [3]:
df_signal_data.shape
Out[3]:
(1567, 592)
In [4]:
df_signal_data.columns
Out[4]:
Index(['Time', '0', '1', '2', '3', '4', '5', '6', '7', '8',
       ...
       '581', '582', '583', '584', '585', '586', '587', '588', '589',
       'Pass/Fail'],
      dtype='object', length=592)
In [5]:
df_signal_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1567 entries, 0 to 1566
Columns: 592 entries, Time to Pass/Fail
dtypes: float64(590), int64(1), object(1)
memory usage: 7.1+ MB
In [6]:
df_signal_data.count()
Out[6]:
Time         1567
0            1561
1            1560
2            1553
3            1553
             ... 
586          1566
587          1566
588          1566
589          1566
Pass/Fail    1567
Length: 592, dtype: int64
In [7]:
df_signal_data.describe()
Out[7]:
0 1 2 3 4 5 6 7 8 9 ... 581 582 583 584 585 586 587 588 589 Pass/Fail
count 1561.000000 1560.000000 1553.000000 1553.000000 1553.000000 1553.0 1553.000000 1558.000000 1565.000000 1565.000000 ... 618.000000 1566.000000 1566.000000 1566.000000 1566.000000 1566.000000 1566.000000 1566.000000 1566.000000 1567.000000
mean 3014.452896 2495.850231 2200.547318 1396.376627 4.197013 100.0 101.112908 0.121822 1.462862 -0.000841 ... 97.934373 0.500096 0.015318 0.003847 3.067826 0.021458 0.016475 0.005283 99.670066 -0.867262
std 73.621787 80.407705 29.513152 441.691640 56.355540 0.0 6.237214 0.008961 0.073897 0.015116 ... 87.520966 0.003404 0.017180 0.003720 3.578033 0.012358 0.008808 0.002867 93.891919 0.498010
min 2743.240000 2158.750000 2060.660000 0.000000 0.681500 100.0 82.131100 0.000000 1.191000 -0.053400 ... 0.000000 0.477800 0.006000 0.001700 1.197500 -0.016900 0.003200 0.001000 0.000000 -1.000000
25% 2966.260000 2452.247500 2181.044400 1081.875800 1.017700 100.0 97.920000 0.121100 1.411200 -0.010800 ... 46.184900 0.497900 0.011600 0.003100 2.306500 0.013425 0.010600 0.003300 44.368600 -1.000000
50% 3011.490000 2499.405000 2201.066700 1285.214400 1.316800 100.0 101.512200 0.122400 1.461600 -0.001300 ... 72.288900 0.500200 0.013800 0.003600 2.757650 0.020500 0.014800 0.004600 71.900500 -1.000000
75% 3056.650000 2538.822500 2218.055500 1591.223500 1.525700 100.0 104.586700 0.123800 1.516900 0.008400 ... 116.539150 0.502375 0.016500 0.004100 3.295175 0.027600 0.020300 0.006400 114.749700 -1.000000
max 3356.350000 2846.440000 2315.266700 3715.041700 1114.536600 100.0 129.252200 0.128600 1.656400 0.074900 ... 737.304800 0.509800 0.476600 0.104500 99.303200 0.102800 0.079900 0.028600 737.304800 1.000000

8 rows × 591 columns

In [8]:
df_signal_data.nunique()
Out[8]:
Time         1534
0            1520
1            1504
2             507
3             518
             ... 
586           322
587           260
588           120
589           611
Pass/Fail       2
Length: 592, dtype: int64
In [9]:
df_signal_data.dtypes
Out[9]:
Time          object
0            float64
1            float64
2            float64
3            float64
              ...   
586          float64
587          float64
588          float64
589          float64
Pass/Fail      int64
Length: 592, dtype: object

B. Print 5 point summary and share at least 2 observations. [3 Marks]

Descriptive Statistics involves understanding the distribution and nature of the data. Five number summary is a part of descriptive statistics and consists of five values and all these values will help us to describe the data.

  • The minimum value (the lowest value)
  • 25th Percentile or Q1
  • 50th Percentile or Q2 or Median
  • 75th Percentile or Q3
  • Maximum Value (the highest value)
In [10]:
df_signal_data.describe().T
Out[10]:
count mean std min 25% 50% 75% max
0 1561.0 3014.452896 73.621787 2743.2400 2966.260000 3011.4900 3056.6500 3356.3500
1 1560.0 2495.850231 80.407705 2158.7500 2452.247500 2499.4050 2538.8225 2846.4400
2 1553.0 2200.547318 29.513152 2060.6600 2181.044400 2201.0667 2218.0555 2315.2667
3 1553.0 1396.376627 441.691640 0.0000 1081.875800 1285.2144 1591.2235 3715.0417
4 1553.0 4.197013 56.355540 0.6815 1.017700 1.3168 1.5257 1114.5366
... ... ... ... ... ... ... ... ...
586 1566.0 0.021458 0.012358 -0.0169 0.013425 0.0205 0.0276 0.1028
587 1566.0 0.016475 0.008808 0.0032 0.010600 0.0148 0.0203 0.0799
588 1566.0 0.005283 0.002867 0.0010 0.003300 0.0046 0.0064 0.0286
589 1566.0 99.670066 93.891919 0.0000 44.368600 71.9005 114.7497 737.3048
Pass/Fail 1567.0 -0.867262 0.498010 -1.0000 -1.000000 -1.0000 -1.0000 1.0000

591 rows × 8 columns

Observations:

- Data contains 591 columns and total of 1567 records
- The 0th column contains 1561 records, 2966 25th percentile (Q1), 3011 50th percentile (Q2), 3356 75th percentile (Q3), 2743 minimum and 3356 maximum, 3014 mean and 73 standard deviation.
  1. Data cleansing: [15 Marks]

A. Write a for loop which will remove all the features with 20%+ Null values and impute rest with mean of the feature. [5 Marks]

In [11]:
# Below code gives percentage of null in every column
null_percentage = df_signal_data.isnull().sum() / df_signal_data.shape[0] * 100
print(null_percentage)

Time         0.000000
0            0.382897
1            0.446713
2            0.893427
3            0.893427
               ...   
586          0.063816
587          0.063816
588          0.063816
589          0.063816
Pass/Fail    0.000000
Length: 592, dtype: float64
In [12]:
# Below code gives list of columns having more than 20% null
columns_to_drop = null_percentage[null_percentage > 0.20].keys()
print(columns_to_drop)

Index(['0', '1', '2', '3', '4', '5', '6', '7', '19', '40',
       ...
       '564', '565', '566', '567', '568', '569', '578', '579', '580', '581'],
      dtype='object', length=338)
In [13]:
for column in df_signal_data.select_dtypes([np.number]).columns:  # Loop through columns
    null_percentage = df_signal_data[column].isnull().sum() / len(df_signal_data)
    null_percentage = null_percentage * 100
    
    if null_percentage > 0.20:
        df_signal_data.drop([column], axis = 1, inplace = True) # drop this column if missing value percentage is more than 20%
    else:
        mean = df_signal_data[column].mean()
        df_signal_data[column] = df_signal_data[column].fillna(mean) # impute missing values with mean of the column if missing value percentage is less than 20%
In [14]:
df_signal_data.columns
Out[14]:
Index(['Time', '8', '9', '10', '11', '12', '13', '14', '15', '16',
       ...
       '577', '582', '583', '584', '585', '586', '587', '588', '589',
       'Pass/Fail'],
      dtype='object', length=254)
In [15]:
# Below code gives percentage of null in every column
null_percentage = df_signal_data.isnull().sum() / df_signal_data.shape[0] * 100
print(null_percentage)

Time         0.0
8            0.0
9            0.0
10           0.0
11           0.0
            ... 
586          0.0
587          0.0
588          0.0
589          0.0
Pass/Fail    0.0
Length: 254, dtype: float64

B. Identify and drop the features which are having same value for all the rows. [3 Marks]

In [16]:
nunique = df_signal_data.nunique()
cols_to_drop = nunique[nunique == 1].index
print('Columns which are unique and to drop: ', cols_to_drop)

Columns which are unique and to drop:  Index(['13', '42', '49', '52', '149', '179', '186', '189', '284', '315', '322',
       '325', '422', '451', '458', '461'],
      dtype='object')
In [17]:
df_signal_data.drop(cols_to_drop, axis = 1, inplace = True)
In [18]:
nunique = df_signal_data.nunique()
cols_to_drop = nunique[nunique == 1].index
print('Columns which are unique and to drop: ', cols_to_drop)

Columns which are unique and to drop:  Index([], dtype='object')

C. Drop other features if required using relevant functional knowledge. Clearly justify the same. [2 Marks]

E. Make all relevant modifications on the data using both functional/logical reasoning/assumptions. [2 Marks]

In [19]:
df_signal_data.isnull().any().any()
Out[19]:
False
In [20]:
# Checking for distribution of the target class shows that the data set is highly imbalanced
df_signal_data['Pass/Fail'].value_counts(normalize = True)
Out[20]:
-1    0.933631
 1    0.066369
Name: Pass/Fail, dtype: float64
In [21]:
# label encoding the target class
# df_signal_data['Pass/Fail'] = df_signal_data['Pass/Fail'].replace([-1, 1], [0, 1])
In [22]:
# Making a copy of the dataset and dropping the target class
df_signal_data_copy = df_signal_data.copy()
df_signal_data_copy.drop(['Pass/Fail'], axis = 1)
Out[22]:
Time 8 9 10 11 12 14 15 16 17 ... 576 577 582 583 584 585 586 587 588 589
0 2008-07-19 11:55:00 1.500500 0.016200 -0.003400 0.945500 202.439600 7.955800 414.871000 10.043300 0.968000 ... 1.6765 14.9509 0.5005 0.0118 0.0035 2.3630 0.021458 0.016475 0.005283 99.670066
1 2008-07-19 12:32:00 1.496600 -0.000500 -0.014800 0.962700 200.547000 10.154800 414.734700 9.259900 0.970100 ... 1.1065 10.9003 0.5019 0.0223 0.0055 4.4447 0.009600 0.020100 0.006000 208.204500
2 2008-07-19 13:17:00 1.443600 0.004100 0.001300 0.961500 202.017900 9.515700 416.707500 9.314400 0.967400 ... 2.0952 9.2721 0.4958 0.0157 0.0039 3.1745 0.058400 0.048400 0.014800 82.860200
3 2008-07-19 14:43:00 1.488200 -0.012400 -0.003300 0.962900 201.848200 9.605200 422.289400 9.692400 0.968700 ... 1.7585 8.5831 0.4990 0.0103 0.0025 2.0544 0.020200 0.014900 0.004400 73.843200
4 2008-07-19 15:22:00 1.503100 -0.003100 -0.007200 0.956900 201.942400 10.566100 420.592500 10.338700 0.973500 ... 1.6597 10.9698 0.4800 0.4766 0.1045 99.3032 0.020200 0.014900 0.004400 73.843200
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1562 2008-10-16 15:13:00 1.342400 -0.004500 -0.005700 0.957900 203.986700 11.769200 419.340400 10.239700 0.969300 ... 1.4879 11.7256 0.4988 0.0143 0.0039 2.8669 0.006800 0.013800 0.004700 203.172000
1563 2008-10-16 20:49:00 1.433300 -0.006100 -0.009300 0.961800 204.017300 9.162000 405.817800 10.228500 0.969600 ... 1.0187 17.8379 0.4975 0.0131 0.0036 2.6238 0.006800 0.013800 0.004700 203.172000
1564 2008-10-17 05:26:00 1.462862 -0.000841 0.000146 0.964353 199.956809 9.005371 413.086035 9.907603 0.971444 ... 1.2237 17.7267 0.4987 0.0153 0.0041 3.0590 0.019700 0.008600 0.002500 43.523100
1565 2008-10-17 06:01:00 1.462200 -0.007200 0.003200 0.969400 197.244800 9.735400 401.915300 9.863000 0.974000 ... 1.7085 19.2104 0.5004 0.0178 0.0038 3.5662 0.026200 0.024500 0.007500 93.494100
1566 2008-10-17 06:07:00 1.462862 -0.000841 0.000146 0.964353 199.956809 9.005371 413.086035 9.907603 0.971444 ... 1.2878 22.9183 0.4987 0.0181 0.0040 3.6275 0.011700 0.016200 0.004500 137.784400

1567 rows × 237 columns

In [23]:
# Create correlation matrix
corr_matrix = df_signal_data_copy.select_dtypes([np.number]).corr().abs()

# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))

# Find features with correlation greater than 0.70
to_drop = [column for column in upper.columns if any(upper[column] > 0.70)]

# Drop features 
df_signal_data_copy.drop(to_drop, axis = 1, inplace=True)

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_10760\4237487680.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
In [24]:
row, column = df_signal_data_copy.shape
print('After dropping the correlated variables the dataset contains', row, 'rows and', column, 'columns')

After dropping the correlated variables the dataset contains 1567 rows and 107 columns
In [25]:
# Boxplot to check for outliers

plt.figure(figsize = (50, 50))
col = 1

for i in df_signal_data_copy.select_dtypes([np.number]).columns:
    plt.subplot(20, 10, col)
    sns.boxplot(df_signal_data_copy[i], color = 'blue')
    col += 1
In [26]:
# Find the outliers and replace them by median

for i in df_signal_data_copy.select_dtypes([np.number]).columns:
    q1 = df_signal_data_copy[i].quantile(0.25)
    q3 = df_signal_data_copy[i].quantile(0.75)
    iqr = q3 - q1
    
    low = q1 - 1.5 * iqr
    high = q3 + 1.5 * iqr
    
    df_signal_data_copy.loc[(df_signal_data_copy[i] < low) | (df_signal_data_copy[i] > high), i] = df_signal_data_copy[i].median()
In [27]:
# After treating the outlier values
    
plt.figure(figsize = (50, 50))
col = 1

for i in df_signal_data_copy.select_dtypes([np.number]).columns:
    plt.subplot(20, 10, col)
    sns.boxplot(df_signal_data_copy[i], color = 'blue')
    col += 1
In [28]:
# Plotting histogram to check for the frequency of values within a variable

df_signal_data_copy.hist(bins = 30, figsize = (40, 40), color = 'blue')
plt.show()
In [29]:
# Density plot to check for the distribution of the variables

plt.figure(figsize = (40, 40))
col = 1

for i in df_signal_data_copy.select_dtypes([np.number]).columns:
    plt.subplot(20, 10, col)
    sns.histplot(df_signal_data_copy[i], color = 'blue')
    col += 1

D. Check for multi-collinearity in the data and take necessary action. [3 Marks]

In [30]:
# Calculate the correlation matrix

def calculate_correlation_matrix(df_signal_data, threshold):
    plt.figure(figsize = (20, 18))
    corr_matrix = df_signal_data.select_dtypes([np.number]).corr()
    sns.heatmap(abs(corr_matrix > 0.7), cmap = "Greens");
In [31]:
# Remove the highly collinear features from dataframe

def remove_collinear_features(df_signal_data, threshold):
    # Calculate the correlation matrix
    corr_matrix = df_signal_data.select_dtypes([np.number]).corr()
    iterations = range(len(corr_matrix.columns) - 1)
    drop_columns = []

    # Iterate through the correlation matrix and compare correlations
    for i in iterations:
        for j in range(i+1):
            item = corr_matrix.iloc[j:(j+1), (i+1):(i+2)]
            col = item.columns
            row = item.index
            val = abs(item.values)

            # If correlation exceeds the threshold
            if val >= threshold:
                # Print the correlated features and the correlation value
                print(col.values[0], "|", row.values[0], "|", round(val[0][0], 2))
                drop_columns.append(col.values[0])

    # Drop one of each pair of correlated columns
    drops = set(drop_cols)
    df_signal_data = df_signal_data.drop(columns = drops, axis = 1, inplace = True)

    return df_signal_data
In [32]:
threshold = 0.70
calculate_correlation_matrix(df_signal_data, threshold)
In [33]:
# Remove columns having more than 70% correlation
# Both positive and negative correlations are considered here

threshold = 0.70
data = remove_collinear_features(df_signal_data, threshold)

17 | 11 | 0.79
22 | 21 | 0.73
26 | 25 | 0.82
27 | 25 | 0.98
27 | 26 | 0.79
30 | 29 | 0.86
34 | 32 | 0.75
35 | 34 | 0.77
36 | 32 | 0.75
36 | 34 | 1.0
36 | 35 | 0.77
39 | 34 | 0.8
39 | 36 | 0.8
46 | 45 | 0.81
50 | 46 | 0.9
51 | 47 | 0.71
147 | 16 | 0.89
148 | 16 | 0.97
148 | 147 | 0.89
152 | 16 | 0.98
152 | 147 | 0.9
152 | 148 | 0.99
154 | 16 | 0.87
154 | 147 | 0.8
154 | 148 | 0.94
154 | 152 | 0.89
163 | 26 | 0.71
163 | 159 | 0.76
164 | 26 | 0.77
164 | 159 | 0.8
164 | 163 | 0.92
165 | 26 | 0.74
165 | 159 | 0.79
165 | 163 | 0.9
165 | 164 | 0.96
174 | 172 | 1.0
185 | 184 | 0.71
187 | 185 | 0.83
248 | 114 | 0.7
249 | 114 | 0.98
249 | 248 | 0.73
252 | 117 | 0.99
254 | 119 | 0.8
279 | 144 | 0.98
280 | 145 | 0.96
281 | 146 | 0.95
282 | 16 | 0.88
282 | 147 | 1.0
282 | 148 | 0.89
282 | 152 | 0.89
282 | 154 | 0.8
283 | 16 | 0.97
283 | 147 | 0.89
283 | 148 | 1.0
283 | 152 | 0.99
283 | 154 | 0.94
283 | 282 | 0.89
285 | 150 | 0.97
286 | 151 | 0.99
287 | 16 | 0.98
287 | 147 | 0.9
287 | 148 | 0.99
287 | 152 | 1.0
287 | 154 | 0.89
287 | 282 | 0.89
287 | 283 | 0.99
288 | 153 | 1.0
289 | 16 | 0.88
289 | 147 | 0.81
289 | 148 | 0.94
289 | 152 | 0.89
289 | 154 | 0.99
289 | 282 | 0.81
289 | 283 | 0.94
289 | 287 | 0.89
291 | 156 | 0.99
294 | 159 | 0.99
294 | 163 | 0.79
294 | 164 | 0.83
294 | 165 | 0.82
295 | 160 | 1.0
296 | 161 | 0.99
297 | 162 | 0.99
298 | 26 | 0.73
298 | 159 | 0.77
298 | 163 | 0.99
298 | 164 | 0.94
298 | 165 | 0.92
298 | 294 | 0.81
299 | 26 | 0.77
299 | 159 | 0.8
299 | 163 | 0.92
299 | 164 | 1.0
299 | 165 | 0.96
299 | 294 | 0.83
299 | 298 | 0.95
300 | 26 | 0.75
300 | 159 | 0.79
300 | 163 | 0.9
300 | 164 | 0.97
300 | 165 | 1.0
300 | 294 | 0.82
300 | 298 | 0.93
300 | 299 | 0.97
301 | 166 | 0.96
302 | 167 | 0.98
303 | 168 | 0.96
304 | 27 | 0.71
304 | 169 | 0.98
305 | 170 | 0.96
306 | 171 | 0.99
307 | 172 | 0.96
307 | 174 | 0.96
308 | 173 | 0.96
309 | 172 | 0.96
309 | 174 | 0.96
309 | 307 | 1.0
310 | 175 | 0.96
311 | 176 | 0.98
312 | 177 | 1.0
316 | 180 | 0.88
317 | 181 | 0.96
318 | 182 | 0.98
319 | 183 | 0.98
320 | 184 | 0.99
320 | 185 | 0.72
321 | 184 | 0.71
321 | 185 | 0.99
321 | 187 | 0.83
321 | 320 | 0.72
323 | 185 | 0.82
323 | 187 | 0.99
323 | 321 | 0.82
324 | 188 | 0.98
356 | 218 | 0.95
359 | 221 | 0.98
360 | 222 | 0.99
361 | 223 | 0.98
365 | 227 | 0.97
366 | 228 | 0.97
376 | 238 | 0.97
377 | 239 | 0.95
386 | 248 | 1.0
386 | 249 | 0.73
387 | 114 | 0.98
387 | 248 | 0.73
387 | 249 | 1.0
387 | 386 | 0.73
388 | 250 | 0.97
389 | 251 | 1.0
390 | 117 | 0.99
390 | 252 | 1.0
392 | 119 | 0.79
392 | 254 | 0.99
393 | 255 | 0.99
417 | 144 | 0.99
417 | 279 | 0.97
420 | 16 | 0.9
420 | 147 | 1.0
420 | 148 | 0.9
420 | 152 | 0.91
420 | 154 | 0.81
420 | 282 | 1.0
420 | 283 | 0.9
420 | 287 | 0.91
420 | 289 | 0.82
421 | 16 | 0.96
421 | 147 | 0.89
421 | 148 | 1.0
421 | 152 | 0.98
421 | 154 | 0.95
421 | 282 | 0.88
421 | 283 | 1.0
421 | 287 | 0.98
421 | 289 | 0.95
421 | 420 | 0.9
424 | 151 | 0.98
424 | 286 | 0.97
425 | 16 | 0.94
425 | 147 | 0.87
425 | 148 | 0.96
425 | 152 | 0.98
425 | 154 | 0.86
425 | 282 | 0.87
425 | 283 | 0.96
425 | 287 | 0.97
425 | 289 | 0.86
425 | 420 | 0.88
425 | 421 | 0.95
426 | 153 | 1.0
426 | 288 | 0.99
427 | 16 | 0.89
427 | 147 | 0.82
427 | 148 | 0.95
427 | 152 | 0.91
427 | 154 | 1.0
427 | 282 | 0.82
427 | 283 | 0.95
427 | 287 | 0.91
427 | 289 | 0.99
427 | 420 | 0.83
427 | 421 | 0.97
427 | 425 | 0.88
429 | 156 | 1.0
429 | 291 | 0.99
430 | 26 | 0.74
430 | 159 | 0.87
430 | 163 | 0.83
430 | 164 | 0.88
430 | 165 | 0.85
430 | 294 | 0.89
430 | 298 | 0.84
430 | 299 | 0.87
430 | 300 | 0.85
431 | 26 | 0.74
431 | 160 | 0.81
431 | 163 | 0.81
431 | 164 | 0.85
431 | 165 | 0.81
431 | 294 | 0.72
431 | 295 | 0.83
431 | 298 | 0.83
431 | 299 | 0.85
431 | 300 | 0.82
431 | 430 | 0.9
434 | 26 | 0.82
434 | 159 | 0.71
434 | 163 | 0.88
434 | 164 | 0.9
434 | 165 | 0.86
434 | 294 | 0.75
434 | 298 | 0.89
434 | 299 | 0.89
434 | 300 | 0.86
434 | 430 | 0.95
434 | 431 | 0.93
435 | 26 | 0.83
435 | 159 | 0.71
435 | 163 | 0.84
435 | 164 | 0.91
435 | 165 | 0.87
435 | 294 | 0.75
435 | 298 | 0.86
435 | 299 | 0.9
435 | 300 | 0.86
435 | 430 | 0.95
435 | 431 | 0.93
435 | 434 | 0.99
436 | 26 | 0.81
436 | 159 | 0.71
436 | 163 | 0.84
436 | 164 | 0.9
436 | 165 | 0.88
436 | 294 | 0.75
436 | 298 | 0.86
436 | 299 | 0.9
436 | 300 | 0.87
436 | 430 | 0.95
436 | 431 | 0.93
436 | 434 | 0.99
436 | 435 | 1.0
437 | 166 | 0.99
437 | 301 | 0.95
439 | 168 | 0.79
439 | 303 | 0.77
440 | 27 | 0.71
440 | 169 | 1.0
440 | 304 | 0.98
441 | 170 | 0.99
441 | 305 | 0.95
442 | 171 | 0.97
442 | 306 | 0.96
443 | 172 | 1.0
443 | 174 | 1.0
443 | 307 | 0.96
443 | 309 | 0.96
444 | 173 | 0.99
444 | 308 | 0.95
445 | 172 | 1.0
445 | 174 | 1.0
445 | 307 | 0.96
445 | 309 | 0.96
445 | 443 | 0.99
446 | 175 | 1.0
446 | 310 | 0.95
447 | 176 | 1.0
447 | 311 | 0.98
448 | 177 | 1.0
448 | 312 | 1.0
452 | 180 | 0.99
452 | 316 | 0.86
453 | 181 | 1.0
453 | 317 | 0.96
454 | 182 | 0.99
454 | 318 | 0.97
455 | 183 | 1.0
455 | 319 | 0.98
456 | 184 | 0.97
456 | 185 | 0.71
456 | 320 | 0.96
456 | 321 | 0.72
457 | 185 | 1.0
457 | 187 | 0.81
457 | 320 | 0.7
457 | 321 | 0.99
457 | 323 | 0.8
457 | 456 | 0.71
459 | 185 | 0.82
459 | 187 | 1.0
459 | 321 | 0.82
459 | 323 | 0.99
459 | 457 | 0.81
490 | 218 | 0.98
490 | 356 | 0.93
493 | 221 | 1.0
493 | 359 | 0.98
494 | 222 | 1.0
494 | 360 | 1.0
495 | 223 | 1.0
495 | 361 | 0.97
520 | 248 | 1.0
520 | 249 | 0.73
520 | 386 | 1.0
520 | 387 | 0.73
522 | 250 | 0.99
522 | 388 | 0.96
523 | 251 | 1.0
523 | 389 | 1.0
524 | 117 | 0.98
524 | 252 | 1.0
524 | 390 | 1.0
526 | 119 | 0.81
526 | 254 | 1.0
526 | 392 | 0.99
527 | 255 | 1.0
527 | 393 | 0.98
545 | 543 | 0.99
560 | 559 | 0.89
561 | 559 | 0.98
561 | 560 | 0.82
573 | 572 | 0.79
574 | 572 | 0.99
574 | 573 | 0.78
575 | 572 | 0.78
575 | 573 | 0.98
575 | 574 | 0.77
576 | 572 | 0.99
576 | 573 | 0.79
576 | 574 | 0.99
576 | 575 | 0.78
577 | 572 | 0.86
577 | 573 | 0.96
577 | 574 | 0.85
577 | 575 | 0.93
577 | 576 | 0.86
584 | 583 | 0.99
585 | 583 | 1.0
585 | 584 | 1.0
588 | 587 | 0.97

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[33], line 5
      1 # Remove columns having more than 70% correlation
      2 # Both positive and negative correlations are considered here
      4 threshold = 0.70
----> 5 data = remove_collinear_features(df_signal_data, threshold)

Cell In[31], line 24, in remove_collinear_features(df_signal_data, threshold)
     21             drop_columns.append(col.values[0])
     23 # Drop one of each pair of correlated columns
---> 24 drops = set(drop_cols)
     25 df_signal_data = df_signal_data.drop(columns = drops, axis = 1, inplace = True)
     27 return df_signal_data

NameError: name 'drop_cols' is not defined
  1. Data analysis & visualisation: [5 Marks]

A. Perform a detailed univariate Analysis with appropriate detailed comments after each analysis. [2 Marks]

In [34]:
df_signal_data.skew()

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_10760\2830632690.py:1: FutureWarning: The default value of numeric_only in DataFrame.skew is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
  df_signal_data.skew()
Out[34]:
8            -0.073824
9             0.331433
10            0.057724
11          -10.221613
12            7.209324
               ...    
586           1.438483
587           1.948028
588           2.030018
589           2.715340
Pass/Fail     3.487359
Length: 237, dtype: float64
In [35]:
# Create correlation matrix
corr_matrix = df_signal_data.select_dtypes([np.number]).corr().abs()

# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))

# Find features with correlation greater than 0.70
to_drop = [column for column in upper.columns if any(upper[column] > 0.70)]

# Drop features 
df_signal_data.drop(to_drop, axis = 1, inplace=True)

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_10760\1222408100.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
In [36]:
row, column = df_signal_data.shape
print('After dropping the correlated variables the dataset contains', row, 'rows and', column, 'columns')

After dropping the correlated variables the dataset contains 1567 rows and 107 columns
In [37]:
# Boxplot to check for outliers

plt.figure(figsize = (50, 50))
col = 1

for i in df_signal_data.select_dtypes([np.number]).columns:
    plt.subplot(20, 10, col)
    sns.boxplot(df_signal_data[i], color = 'blue')
    col += 1
In [38]:
# Plotting histogram to check for the frequency of values within a variable

df_signal_data.hist(bins = 30, figsize = (40, 40), color = 'blue')
plt.show()
In [39]:
# Density plot to check for the distribution of the variables

plt.figure(figsize = (40, 40))
col = 1

for i in df_signal_data.select_dtypes([np.number]).columns:
    plt.subplot(20, 10, col)
    sns.histplot(df_signal_data[i], color = 'blue')
    col += 1

B. Perform bivariate and multivariate analysis with appropriate detailed comments after each analysis. [3 Marks]

In [40]:
plt.style.use('seaborn-deep')
plt.rcParams['figure.figsize'] = (5, 5)
plt.pie(df_signal_data['Pass/Fail'].value_counts(), labels = ['Pass', 'Fail'], colors = ['blue', 'green'], explode = [0, 0.1], autopct = "%.2f%%", shadow = True)
plt.axis('off')
plt.title('Target: Pass or Fail', fontsize = 20)
plt.legend()
plt.show()
df_signal_data['Pass/Fail'].value_counts().plot(kind="bar");

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_10760\1510467266.py:1: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
  plt.style.use('seaborn-deep')
In [41]:
# heatmap to get correlation

plt.rcParams['figure.figsize'] = (18, 18)
sns.heatmap(df_signal_data.corr(), cmap = "YlGnBu")
plt.title('Correlation heatmap for the Data', fontsize = 20)

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_10760\1586070570.py:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(df_signal_data.corr(), cmap = "YlGnBu")
Out[41]:
Text(0.5, 1.0, 'Correlation heatmap for the Data')
  1. Data pre-processing: [10 Marks]

A. Segregate predictors vs target attributes. [2 Marks]

In [42]:
# Deleting the first column 

df_signal_data = df_signal_data.drop(columns='Time')
df_signal_data.head()
Out[42]:
8 9 10 11 12 14 15 16 18 20 ... 559 570 571 572 582 583 586 587 589 Pass/Fail
0 1.5005 0.0162 -0.0034 0.9455 202.4396 7.9558 414.8710 10.0433 192.3963 1.4026 ... 0.4385 533.8500 2.1113 8.95 0.5005 0.0118 0.021458 0.016475 99.670066 -1
1 1.4966 -0.0005 -0.0148 0.9627 200.5470 10.1548 414.7347 9.2599 191.2872 1.3825 ... 0.1745 535.0164 2.4335 5.92 0.5019 0.0223 0.009600 0.020100 208.204500 -1
2 1.4436 0.0041 0.0013 0.9615 202.0179 9.5157 416.7075 9.3144 192.7035 1.4123 ... 0.3718 535.0245 2.0293 11.21 0.4958 0.0157 0.058400 0.048400 82.860200 1
3 1.4882 -0.0124 -0.0033 0.9629 201.8482 9.6052 422.2894 9.6924 192.1557 1.4011 ... 0.7288 530.5682 2.0253 9.33 0.4990 0.0103 0.020200 0.014900 73.843200 -1
4 1.5031 -0.0031 -0.0072 0.9569 201.9424 10.5661 420.5925 10.3387 191.6037 1.3888 ... 0.2156 532.0155 2.0275 8.83 0.4800 0.4766 0.020200 0.014900 73.843200 -1

5 rows × 106 columns

In [43]:
# separating the dependent and independent data

x = df_signal_data.iloc[:, :106]
y = df_signal_data["Pass/Fail"]

# getting the shapes of new data sets x and y
print("shape of x:", x.shape)
print("shape of y:", y.shape)

shape of x: (1567, 106)
shape of y: (1567,)
In [44]:
# splitting them into train test and split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)

# gettiing the shapes
print("shape of x_train: ", x_train.shape)
print("shape of x_test: ", x_test.shape)
print("shape of y_train: ", y_train.shape)
print("shape of y_test: ", y_test.shape)

shape of x_train:  (1096, 106)
shape of x_test:  (471, 106)
shape of y_train:  (1096,)
shape of y_test:  (471,)
In [45]:
print("{0:0.2f}% data is in training set".format((len(x_train) / len(df_signal_data.index)) * 100))
print("{0:0.2f}% data is in test set".format((len(x_test) / len(df_signal_data.index)) * 100))

69.94% data is in training set
30.06% data is in test set
In [46]:
print("Training Fail    : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 1]), (len(y_train[y_train[:] == 1])/len(y_train)) * 100))
print("Training Pass  : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 0]), (len(y_train[y_train[:] == 0])/len(y_train)) * 100))
print("")
print("Test Fail        : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 1]), (len(y_test[y_test[:] == 1])/len(y_test)) * 100))
print("Test Pass       : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 0]), (len(y_test[y_test[:] == 0])/len(y_test)) * 100))
print("")

Training Fail    : 72 (6.57%)
Training Pass  : 0 (0.00%)

Test Fail        : 32 (6.79%)
Test Pass       : 0 (0.00%)
In [47]:
logisticRegression = LogisticRegression()
logisticRegression.fit(x_train, y_train)

C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Out[47]:
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
In [48]:
predicted_labels = logisticRegression.predict(x_test)

print('Accuracy on Training data: ', logisticRegression.score(x_train, y_train))
print('Accuracy on Test data: ', logisticRegression.score(x_test, y_test))

Accuracy on Training data:  0.9324817518248175
Accuracy on Test data:  0.9299363057324841
In [49]:
confusion_matrix(y_test, predicted_labels)
Out[49]:
array([[438,   1],
       [ 32,   0]], dtype=int64)
In [50]:
# calculate accuracy measures and confusion matrix

cm = confusion_matrix(y_test, predicted_labels)
df_cm = pd.DataFrame(cm, index = [i for i in ["Pass","Fail"]], columns = [i for i in ["Pass","Fail"]])
plt.figure(figsize = (7, 5))
sns.heatmap(df_cm, annot = True, fmt = 'g')
plt.show()
In [51]:
classification_report(y_test, predicted_labels)
Out[51]:
'              precision    recall  f1-score   support\n\n          -1       0.93      1.00      0.96       439\n           1       0.00      0.00      0.00        32\n\n    accuracy                           0.93       471\n   macro avg       0.47      0.50      0.48       471\nweighted avg       0.87      0.93      0.90       471\n'

B. Check for target balancing and fix it if found imbalanced. [3 Marks]

D. Check if the train and test data have similar statistical characteristics when compared with original data. [2 Marks]

In [52]:
# # Initializaing various classification algorithms with normal dataset and choosing the best model based on f1 score for tuning

# seed = 3

# # prepare models
# models = []
# models.append(('LR', LogisticRegression()))
# models.append(('KNN', KNeighborsClassifier(n_neighbors=13)))
# models.append(('GNB', GaussianNB()))
# #models.append(("SVM", SVC(kernel='linear', C=1, gamma=.6)))
# models.append(("DT", DecisionTreeClassifier()))
# models.append(("RF", RandomForestClassifier()))
# models.append(("AB", AdaBoostClassifier()))
# models.append(("GBT", GradientBoostingClassifier()))
# #models.append(("XGB", XGBClassifier(verbosity=0)))
# #models.append(("LightGBM",LGBMClassifier()))

# # evaluate each model in turn
# results = []
# names = []
# scoring = 'accuracy'

# for name, model in models:
#     kfold = model_selection.KFold(n_splits = 5, random_state = seed, shuffle = True)
#     cv_results = model_selection.cross_val_score(model, x, y, cv = kfold, scoring = scoring)
#     results.append(cv_results)
#     names.append(name)
#     msg = "%s: %f (%f)" % (name, cv_results.mean() * 100, cv_results.std() * 100)
#     print(msg)
In [53]:
# Implementing random under sampling

randomUnderSampler = RandomUnderSampler(sampling_strategy = 0.5)
x_under, y_under= randomUnderSampler.fit_resample(x_train, y_train)
In [54]:
print("Under Training Fail    : {0} ({1:0.2f}%)".format(len(y_under[y_under[:] == 1]), (len(y_under[y_under[:] == 1])/len(y_under)) * 100))
print("under Training Pass  : {0} ({1:0.2f}%)".format(len(y_under[y_under[:] == 0]), (len(y_under[y_under[:] == 0])/len(y_under)) * 100))

Under Training Fail    : 72 (33.33%)
under Training Pass  : 0 (0.00%)
In [55]:
# Implementing SMOTE

smote = SMOTE(sampling_strategy = 0.5)
x_SMOTE, y_SMOTE = smote.fit_resample(x_train, y_train)
In [56]:
print("SMOTE Training Fail    : {0} ({1:0.2f}%)".format(len(y_SMOTE[y_SMOTE[:] == 1]), (len(y_SMOTE[y_SMOTE[:] == 1])/len(y_SMOTE)) * 100))
print("SMOTE Training Pass  : {0} ({1:0.2f}%)".format(len(y_SMOTE[y_SMOTE[:] == 0]), (len(y_SMOTE[y_SMOTE[:] == 0])/len(y_SMOTE)) * 100))

SMOTE Training Fail    : 512 (33.33%)
SMOTE Training Pass  : 0 (0.00%)
In [57]:
# Implementing random over sampling

randomOverSampler = RandomOverSampler(sampling_strategy = 0.5)
x_over, y_over = randomOverSampler.fit_resample(x_train, y_train)
In [58]:
print("over Training Fail    : {0} ({1:0.2f}%)".format(len(y_over[y_over[:] == 1]), (len(y_over[y_over[:] == 1])/len(y_over)) * 100))
print("over Training Pass  : {0} ({1:0.2f}%)".format(len(y_over[y_over[:] == 0]), (len(y_over[y_over[:] == 0])/len(y_over)) * 100))

over Training Fail    : 512 (33.33%)
over Training Pass  : 0 (0.00%)
In [59]:
# Implementing ADASYN sampling

adasyn = ADASYN(sampling_strategy=0.5)
x_adasyn, y_adasyn = adasyn.fit_resample(x_train, y_train)
In [60]:
print("ADASYN Training Fail    : {0} ({1:0.2f}%)".format(len(y_adasyn[y_adasyn[:] == 1]), (len(y_adasyn[y_adasyn[:] == 1])/len(y_adasyn)) * 100))
print("ADASYN Training Pass  : {0} ({1:0.2f}%)".format(len(y_adasyn[y_adasyn[:] == 0]), (len(y_adasyn[y_adasyn[:] == 0])/len(y_adasyn)) * 100))

ADASYN Training Fail    : 527 (33.98%)
ADASYN Training Pass  : 0 (0.00%)
In [61]:
# # Initializaing various classification algorithms with normal dataset and choosing the best model based on f1 score for tuning

# seed = 3

# # prepare models
# models = []
# models.append(('LR', LogisticRegression()))
# models.append(('KNN', KNeighborsClassifier(n_neighbors=13)))
# models.append(('GNB', GaussianNB()))
# models.append(("SVM", SVC(kernel='linear', C=1, gamma=.6)))
# models.append(("DT", DecisionTreeClassifier()))
# models.append(("RF", RandomForestClassifier()))
# models.append(("AB", AdaBoostClassifier()))
# models.append(("GBT", GradientBoostingClassifier()))
# #models.append(("XGB", XGBClassifier(verbosity=0)))
# #models.append(("LightGBM",LGBMClassifier()))

# # evaluate each model in turn
# results = []
# names = []
# scoring = 'accuracy'

# for name, model in models:
#     kfold = model_selection.KFold(n_splits = 5, random_state = seed, shuffle = True)
#     cv_results = model_selection.cross_val_score(model, x, y, cv = kfold, scoring = scoring)
#     results.append(cv_results)
#     names.append(name)
#     msg = "%s: %f (%f)" % (name, cv_results.mean() * 100, cv_results.std() * 100)
#     print(msg)

C. Perform train-test split and standardise the data or vice versa if required. [3 Marks]

In [62]:
x.head()
Out[62]:
8 9 10 11 12 14 15 16 18 20 ... 559 570 571 572 582 583 586 587 589 Pass/Fail
0 1.5005 0.0162 -0.0034 0.9455 202.4396 7.9558 414.8710 10.0433 192.3963 1.4026 ... 0.4385 533.8500 2.1113 8.95 0.5005 0.0118 0.021458 0.016475 99.670066 -1
1 1.4966 -0.0005 -0.0148 0.9627 200.5470 10.1548 414.7347 9.2599 191.2872 1.3825 ... 0.1745 535.0164 2.4335 5.92 0.5019 0.0223 0.009600 0.020100 208.204500 -1
2 1.4436 0.0041 0.0013 0.9615 202.0179 9.5157 416.7075 9.3144 192.7035 1.4123 ... 0.3718 535.0245 2.0293 11.21 0.4958 0.0157 0.058400 0.048400 82.860200 1
3 1.4882 -0.0124 -0.0033 0.9629 201.8482 9.6052 422.2894 9.6924 192.1557 1.4011 ... 0.7288 530.5682 2.0253 9.33 0.4990 0.0103 0.020200 0.014900 73.843200 -1
4 1.5031 -0.0031 -0.0072 0.9569 201.9424 10.5661 420.5925 10.3387 191.6037 1.3888 ... 0.2156 532.0155 2.0275 8.83 0.4800 0.4766 0.020200 0.014900 73.843200 -1

5 rows × 106 columns

In [63]:
# Split x and y into training and test set in 70:30 ratio
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 10)
In [64]:
print("{0:0.2f}% data is in training set".format((len(x_train) / len(df_signal_data.index)) * 100))
print("{0:0.2f}% data is in test set".format((len(x_test) / len(df_signal_data.index)) * 100))

69.94% data is in training set
30.06% data is in test set
In [65]:
# Standardize the data training data

scaler = preprocessing.StandardScaler().fit(x_train)
x_train_scaled = scaler.transform(x_train)
In [66]:
# Standardize the data test data

scaler = preprocessing.StandardScaler().fit(x_test)
x_test_scaled = scaler.transform(x_test)
In [67]:
model = SVC()
model.fit(x_train_scaled, y_train)
prediction = model.predict(x_test_scaled)
In [68]:
# Check the accuracy on the training data
print('Accuracy on Training data: ', model.score(x_train, y_train))

# Check the accuracy on the testing data
print('Accuracy on Testing data: ', model.score(x_test, y_test))

# Calculate the recall value 
print('Recall value: ', metrics.recall_score(y_test, prediction, average = 'macro'))

# Calculate the precision value 
print('Precision value: ', metrics.precision_score(y_test, prediction, average = 'macro'))

C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\base.py:413: UserWarning: X has feature names, but SVC was fitted without feature names
  warnings.warn(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\base.py:413: UserWarning: X has feature names, but SVC was fitted without feature names
  warnings.warn(

Accuracy on Training data:  0.9315693430656934
Accuracy on Testing data:  0.9384288747346072
Recall value:  0.9137931034482758
Precision value:  0.9944071588366891
In [69]:
print("Confusion Matrix:\n", metrics.confusion_matrix(prediction, y_test))

Confusion Matrix:
 [[442   5]
 [  0  24]]
In [70]:
print("Classification Report:\n", metrics.classification_report(prediction, y_test))

Classification Report:
               precision    recall  f1-score   support

          -1       1.00      0.99      0.99       447
           1       0.83      1.00      0.91        24

    accuracy                           0.99       471
   macro avg       0.91      0.99      0.95       471
weighted avg       0.99      0.99      0.99       471
  1. Model training, testing and tuning: [20 Marks]

A. Use any Supervised Learning technique to train a model. [2 Marks]

In [71]:
x.head()
Out[71]:
8 9 10 11 12 14 15 16 18 20 ... 559 570 571 572 582 583 586 587 589 Pass/Fail
0 1.5005 0.0162 -0.0034 0.9455 202.4396 7.9558 414.8710 10.0433 192.3963 1.4026 ... 0.4385 533.8500 2.1113 8.95 0.5005 0.0118 0.021458 0.016475 99.670066 -1
1 1.4966 -0.0005 -0.0148 0.9627 200.5470 10.1548 414.7347 9.2599 191.2872 1.3825 ... 0.1745 535.0164 2.4335 5.92 0.5019 0.0223 0.009600 0.020100 208.204500 -1
2 1.4436 0.0041 0.0013 0.9615 202.0179 9.5157 416.7075 9.3144 192.7035 1.4123 ... 0.3718 535.0245 2.0293 11.21 0.4958 0.0157 0.058400 0.048400 82.860200 1
3 1.4882 -0.0124 -0.0033 0.9629 201.8482 9.6052 422.2894 9.6924 192.1557 1.4011 ... 0.7288 530.5682 2.0253 9.33 0.4990 0.0103 0.020200 0.014900 73.843200 -1
4 1.5031 -0.0031 -0.0072 0.9569 201.9424 10.5661 420.5925 10.3387 191.6037 1.3888 ... 0.2156 532.0155 2.0275 8.83 0.4800 0.4766 0.020200 0.014900 73.843200 -1

5 rows × 106 columns

In [72]:
# convert the features into z scores as we do not know what units / scales were used and store them in new dataframe
# It is always adviced to scale numeric attributes in models that calculate distances.

x_Scaled = x.apply(zscore)  # convert all attributes to Z scale
x_Scaled.describe().T
Out[72]:
count mean std min 25% 50% 75% max
8 1567.0 3.137815e-15 1.000319 -3.682475 -0.699101 -0.017090 0.731294 2.621555
9 1567.0 9.068829e-18 1.000319 -3.480332 -0.659460 -0.030392 0.611919 5.015393
10 1567.0 0.000000e+00 1.000319 -3.771119 -0.618275 0.027359 0.619189 5.687413
11 1567.0 -3.827046e-15 1.000319 -24.835180 -0.502630 0.116335 0.558453 1.643651
12 1567.0 1.242430e-14 1.000319 -5.489229 -0.561085 -0.128976 0.629945 22.152683
... ... ... ... ... ... ... ... ...
583 1567.0 -5.668018e-18 1.000319 -0.542709 -0.216539 -0.088401 0.068860 26.867231
586 1567.0 1.360324e-16 1.000319 -3.105874 -0.648441 -0.077604 0.497282 6.586209
587 1567.0 9.068829e-18 1.000319 -1.508184 -0.667458 -0.190289 0.434576 7.205831
589 1567.0 1.768422e-16 1.000319 -1.062218 -0.589367 -0.294644 0.160709 6.795495
Pass/Fail 1567.0 -4.534414e-18 1.000319 -0.266621 -0.266621 -0.266621 -0.266621 3.750641

106 rows × 8 columns

In [73]:
# Split x and y into training and test set in 70:30 ratio

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, random_state = 1)
In [74]:
print("{0:0.2f}% data is in training set".format((len(x_train) / len(df_signal_data.index)) * 100))
print("{0:0.2f}% data is in test set".format((len(x_test) / len(df_signal_data.index)) * 100))

69.94% data is in training set
30.06% data is in test set
In [75]:
KNN = KNeighborsClassifier(n_neighbors= 5, metric = 'euclidean')
KNN.fit(x_train, y_train)
Out[75]:
KNeighborsClassifier(metric='euclidean')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(metric='euclidean')
In [76]:
# For every test data point, predict it's label based on 5 nearest neighbours in this model. The majority class will 
# be assigned to the test data point

predicted_labels = KNN.predict(x_test)

print('Accuracy on Training data: ', KNN.score(x_train, y_train))
print('Accuracy on Test data: ', KNN.score(x_test, y_test))

Accuracy on Training data:  0.9361313868613139
Accuracy on Test data:  0.9320594479830149
In [77]:
confusion_matrix(y_test, predicted_labels)
Out[77]:
array([[438,   1],
       [ 31,   1]], dtype=int64)
In [78]:
# calculate accuracy measures and confusion matrix

cm = confusion_matrix(y_test, predicted_labels)
df_cm = pd.DataFrame(cm, index = [i for i in ["Pass", "Fail"]], columns = [i for i in ["Pass", "Fail"]])
plt.figure(figsize = (7, 5))
sns.heatmap(df_cm, annot = True, fmt = 'g')
plt.show()
In [79]:
classification_report(y_test, predicted_labels)
Out[79]:
'              precision    recall  f1-score   support\n\n          -1       0.93      1.00      0.96       439\n           1       0.50      0.03      0.06        32\n\n    accuracy                           0.93       471\n   macro avg       0.72      0.51      0.51       471\nweighted avg       0.90      0.93      0.90       471\n'

B. Use cross validation techniques. [3 Marks]

Hint: Use all CV techniques that you have learnt in the course.

In [80]:
x.head()
Out[80]:
8 9 10 11 12 14 15 16 18 20 ... 559 570 571 572 582 583 586 587 589 Pass/Fail
0 1.5005 0.0162 -0.0034 0.9455 202.4396 7.9558 414.8710 10.0433 192.3963 1.4026 ... 0.4385 533.8500 2.1113 8.95 0.5005 0.0118 0.021458 0.016475 99.670066 -1
1 1.4966 -0.0005 -0.0148 0.9627 200.5470 10.1548 414.7347 9.2599 191.2872 1.3825 ... 0.1745 535.0164 2.4335 5.92 0.5019 0.0223 0.009600 0.020100 208.204500 -1
2 1.4436 0.0041 0.0013 0.9615 202.0179 9.5157 416.7075 9.3144 192.7035 1.4123 ... 0.3718 535.0245 2.0293 11.21 0.4958 0.0157 0.058400 0.048400 82.860200 1
3 1.4882 -0.0124 -0.0033 0.9629 201.8482 9.6052 422.2894 9.6924 192.1557 1.4011 ... 0.7288 530.5682 2.0253 9.33 0.4990 0.0103 0.020200 0.014900 73.843200 -1
4 1.5031 -0.0031 -0.0072 0.9569 201.9424 10.5661 420.5925 10.3387 191.6037 1.3888 ... 0.2156 532.0155 2.0275 8.83 0.4800 0.4766 0.020200 0.014900 73.843200 -1

5 rows × 106 columns

In [81]:
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, random_state = 1)
In [82]:
# Implementing K-fold cross-validation

num_folds = 50
seed = 7

kfold = KFold(n_splits = num_folds, random_state = seed, shuffle = True)
model = LogisticRegression()
results = cross_val_score(model, x, y, cv = kfold)

C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
In [83]:
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean() * 100.0, results.std() * 100.0))

Accuracy: 93.216% (5.051%)
In [84]:
print("Cross Validation score: {}".format(results))

Cross Validation score: [1.         0.9375     0.9375     0.96875    0.96875    0.90625
 0.9375     0.96875    0.96875    1.         0.9375     0.875
 1.         0.9375     0.875      1.         0.9375     0.93548387
 0.87096774 0.90322581 0.87096774 0.93548387 0.93548387 0.93548387
 0.93548387 1.         1.         0.90322581 0.93548387 0.93548387
 0.87096774 0.87096774 0.93548387 0.87096774 0.90322581 0.77419355
 0.87096774 0.96774194 0.90322581 1.         0.96774194 0.90322581
 0.96774194 0.93548387 0.83870968 0.90322581 0.87096774 1.
 1.         1.        ]
In [85]:
print("Average Cross Validation score: {}".format(results.mean()))

Average Cross Validation score: 0.9321572580645161
In [ ]:
In [86]:
# Implementing Stratified k-fold cross-validation

num_folds = 3
seed = 7

stratifiedKFold = StratifiedKFold(n_splits = num_folds, random_state = seed, shuffle = True)
model = LogisticRegression()
results = cross_val_score(model, x, y, cv = stratifiedKFold)

C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
In [87]:
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean() * 100.0, results.std() * 100.0))

Accuracy: 93.172% (0.087%)
In [88]:
print("Cross Validation score: {}".format(results))

Cross Validation score: [0.93116635 0.93295019 0.93103448]
In [89]:
print("Average Cross Validation score: {}".format(results.mean()))

Average Cross Validation score: 0.9317170074406178
In [ ]:
In [90]:
# Implementing Leave-p-out cross-validation

num_folds = 5

leavePOut = LeavePOut(p = 1)
leavePOut.get_n_splits(x)
model = RandomForestClassifier(n_estimators = num_folds, max_depth = 3, n_jobs = -1)
results = cross_val_score(model, x, y, cv = leavePOut)
In [91]:
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean() * 100.0, results.std() * 100.0))

Accuracy: 94.065% (23.628%)
In [92]:
print("Cross Validation score: {}".format(results))

Cross Validation score: [1. 1. 1. ... 1. 1. 1.]
In [93]:
print("Average Cross Validation score: {}".format(results.mean()))

Average Cross Validation score: 0.9406509253350351
In [ ]:
In [94]:
# Implementing Leave-one-out cross-validation

num_folds = 7

leaveOneOut = LeaveOneOut()
leavePOut.get_n_splits(x)
model = RandomForestClassifier(n_estimators = num_folds, max_depth = 4, n_jobs = -1)
results = cross_val_score(model, x, y, cv = leaveOneOut)
In [95]:
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean() * 100.0, results.std() * 100.0))

Accuracy: 94.193% (23.388%)
In [96]:
print("Cross Validation score: {}".format(results))

Cross Validation score: [1. 1. 0. ... 1. 1. 1.]
In [97]:
print("Average Cross Validation score: {}".format(results.mean()))

Average Cross Validation score: 0.9419272495213784
In [ ]:
In [98]:
# Implementing ShuffleSplit cross-validation

num_folds = 6

shuffleSplit = ShuffleSplit(n_splits = num_folds, test_size = 0.3, train_size = 0.5)
model = LogisticRegression()
results = cross_val_score(model, x, y, cv = shuffleSplit)

C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
In [99]:
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean() * 100.0, results.std() * 100.0))

Accuracy: 93.489% (1.121%)
In [100]:
print("Cross Validation score: {}".format(results))

Cross Validation score: [0.92356688 0.92356688 0.95116773 0.94904459 0.92993631 0.93205945]
In [101]:
print("Average Cross Validation score: {}".format(results.mean()))

Average Cross Validation score: 0.9348903043170559

C. Apply hyper-parameter tuning techniques to get the best accuracy. [3 Marks]

Suggestion: Use all possible hyper parameter combinations to extract the best accuracies.

In [102]:
x.head()
Out[102]:
8 9 10 11 12 14 15 16 18 20 ... 559 570 571 572 582 583 586 587 589 Pass/Fail
0 1.5005 0.0162 -0.0034 0.9455 202.4396 7.9558 414.8710 10.0433 192.3963 1.4026 ... 0.4385 533.8500 2.1113 8.95 0.5005 0.0118 0.021458 0.016475 99.670066 -1
1 1.4966 -0.0005 -0.0148 0.9627 200.5470 10.1548 414.7347 9.2599 191.2872 1.3825 ... 0.1745 535.0164 2.4335 5.92 0.5019 0.0223 0.009600 0.020100 208.204500 -1
2 1.4436 0.0041 0.0013 0.9615 202.0179 9.5157 416.7075 9.3144 192.7035 1.4123 ... 0.3718 535.0245 2.0293 11.21 0.4958 0.0157 0.058400 0.048400 82.860200 1
3 1.4882 -0.0124 -0.0033 0.9629 201.8482 9.6052 422.2894 9.6924 192.1557 1.4011 ... 0.7288 530.5682 2.0253 9.33 0.4990 0.0103 0.020200 0.014900 73.843200 -1
4 1.5031 -0.0031 -0.0072 0.9569 201.9424 10.5661 420.5925 10.3387 191.6037 1.3888 ... 0.2156 532.0155 2.0275 8.83 0.4800 0.4766 0.020200 0.014900 73.843200 -1

5 rows × 106 columns

In [103]:
x_train, x_test, y_train, y_test = train_test_split(x, y, stratify = y, random_state = 7)
In [104]:
print("{0:0.2f}% data is in training set".format((len(x_train) / len(df_signal_data.index)) * 100))
print("{0:0.2f}% data is in test set".format((len(x_test) / len(df_signal_data.index)) * 100))

74.98% data is in training set
25.02% data is in test set
In [105]:
knn_clf = KNeighborsClassifier()
knn_clf.fit(x_train, y_train)
Out[105]:
KNeighborsClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier()
In [106]:
param_grid = {'n_neighbors': list(range(1, 9)), 'algorithm': ('auto', 'ball_tree', 'kd_tree' , 'brute')}
gs = GridSearchCV(knn_clf, param_grid, cv = 10)
gs.fit(x_train, y_train)
Out[106]:
GridSearchCV(cv=10, estimator=KNeighborsClassifier(),
             param_grid={'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute'),
                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8]})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=10, estimator=KNeighborsClassifier(),
             param_grid={'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute'),
                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8]})
KNeighborsClassifier()
KNeighborsClassifier()
In [107]:
gs.best_params_
Out[107]:
{'algorithm': 'auto', 'n_neighbors': 6}
In [108]:
gs.cv_results_['params']
Out[108]:
[{'algorithm': 'auto', 'n_neighbors': 1},
 {'algorithm': 'auto', 'n_neighbors': 2},
 {'algorithm': 'auto', 'n_neighbors': 3},
 {'algorithm': 'auto', 'n_neighbors': 4},
 {'algorithm': 'auto', 'n_neighbors': 5},
 {'algorithm': 'auto', 'n_neighbors': 6},
 {'algorithm': 'auto', 'n_neighbors': 7},
 {'algorithm': 'auto', 'n_neighbors': 8},
 {'algorithm': 'ball_tree', 'n_neighbors': 1},
 {'algorithm': 'ball_tree', 'n_neighbors': 2},
 {'algorithm': 'ball_tree', 'n_neighbors': 3},
 {'algorithm': 'ball_tree', 'n_neighbors': 4},
 {'algorithm': 'ball_tree', 'n_neighbors': 5},
 {'algorithm': 'ball_tree', 'n_neighbors': 6},
 {'algorithm': 'ball_tree', 'n_neighbors': 7},
 {'algorithm': 'ball_tree', 'n_neighbors': 8},
 {'algorithm': 'kd_tree', 'n_neighbors': 1},
 {'algorithm': 'kd_tree', 'n_neighbors': 2},
 {'algorithm': 'kd_tree', 'n_neighbors': 3},
 {'algorithm': 'kd_tree', 'n_neighbors': 4},
 {'algorithm': 'kd_tree', 'n_neighbors': 5},
 {'algorithm': 'kd_tree', 'n_neighbors': 6},
 {'algorithm': 'kd_tree', 'n_neighbors': 7},
 {'algorithm': 'kd_tree', 'n_neighbors': 8},
 {'algorithm': 'brute', 'n_neighbors': 1},
 {'algorithm': 'brute', 'n_neighbors': 2},
 {'algorithm': 'brute', 'n_neighbors': 3},
 {'algorithm': 'brute', 'n_neighbors': 4},
 {'algorithm': 'brute', 'n_neighbors': 5},
 {'algorithm': 'brute', 'n_neighbors': 6},
 {'algorithm': 'brute', 'n_neighbors': 7},
 {'algorithm': 'brute', 'n_neighbors': 8}]
In [109]:
gs.cv_results_['mean_test_score']
Out[109]:
array([0.88174707, 0.92937853, 0.92088223, 0.9327756 , 0.9319209 ,
       0.93362306, 0.93362306, 0.93362306, 0.88174707, 0.92937853,
       0.92088223, 0.9327756 , 0.9319209 , 0.93362306, 0.93362306,
       0.93362306, 0.88174707, 0.92937853, 0.92088223, 0.9327756 ,
       0.9319209 , 0.93362306, 0.93362306, 0.93362306, 0.88174707,
       0.92937853, 0.92088223, 0.9327756 , 0.9319209 , 0.93362306,
       0.93362306, 0.93362306])

D. Use any other technique/method which can enhance the model performance. [4 Marks]

Hint: Dimensionality reduction, attribute removal, standardisation/normalisation, target balancing etc.

In [110]:
x.head()
Out[110]:
8 9 10 11 12 14 15 16 18 20 ... 559 570 571 572 582 583 586 587 589 Pass/Fail
0 1.5005 0.0162 -0.0034 0.9455 202.4396 7.9558 414.8710 10.0433 192.3963 1.4026 ... 0.4385 533.8500 2.1113 8.95 0.5005 0.0118 0.021458 0.016475 99.670066 -1
1 1.4966 -0.0005 -0.0148 0.9627 200.5470 10.1548 414.7347 9.2599 191.2872 1.3825 ... 0.1745 535.0164 2.4335 5.92 0.5019 0.0223 0.009600 0.020100 208.204500 -1
2 1.4436 0.0041 0.0013 0.9615 202.0179 9.5157 416.7075 9.3144 192.7035 1.4123 ... 0.3718 535.0245 2.0293 11.21 0.4958 0.0157 0.058400 0.048400 82.860200 1
3 1.4882 -0.0124 -0.0033 0.9629 201.8482 9.6052 422.2894 9.6924 192.1557 1.4011 ... 0.7288 530.5682 2.0253 9.33 0.4990 0.0103 0.020200 0.014900 73.843200 -1
4 1.5031 -0.0031 -0.0072 0.9569 201.9424 10.5661 420.5925 10.3387 191.6037 1.3888 ... 0.2156 532.0155 2.0275 8.83 0.4800 0.4766 0.020200 0.014900 73.843200 -1

5 rows × 106 columns

In [111]:
# Split x and y into training and test set in 70:30 ratio

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, random_state = 1)
In [112]:
print("{0:0.2f}% data is in training set".format((len(x_train) / len(df_signal_data.index)) * 100))
print("{0:0.2f}% data is in test set".format((len(x_test) / len(df_signal_data.index)) * 100))

69.94% data is in training set
30.06% data is in test set
In [113]:
pipe_lr = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components = 2)), ('clf', LogisticRegression(random_state = 1))]) 
pipe_lr.fit(x_train, y_train) 
print('Test Accuracy: %.3f' % pipe_lr.score(x_test, y_test))

Test Accuracy: 0.932
In [ ]:
In [114]:
pipe_svc = Pipeline([('scl', StandardScaler()), ('pca', PCA()), ('svc', SVC())]) 

param_grid = {'pca__n_components': [14, 15], 'svc__C': [0.001, 0.01, 0.1, 1, 10, 100], 'svc__gamma': [0.001, 0.01, 0.1, 1, 10], 'svc__kernel': ['rbf','poly']} 

grid = GridSearchCV(pipe_svc , param_grid = param_grid, cv = 5) 

grid.fit(x_train, y_train)

print("Best cross-validation accuracy: {:.2f}". format(grid.best_score_)) 
print("Best parameters: ", grid.best_params_) 
print("Test set accuracy: {:.2f}". format(grid.score(x_test, y_test)))

Best cross-validation accuracy: 0.93
Best parameters:  {'pca__n_components': 14, 'svc__C': 0.001, 'svc__gamma': 0.1, 'svc__kernel': 'poly'}
Test set accuracy: 0.93
In [115]:
grid.predict(x_test)
Out[115]:
array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1,  1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
        1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], dtype=int64)
In [ ]:
In [116]:
# build a classifier
clf = RandomForestClassifier(n_estimators = 50)
In [117]:
# specify parameters and distributions to sample from
param_dist = {"max_depth": [3, None],
              "max_features": sp_randint(1, 11),
              "min_samples_split": sp_randint(2, 11),
              "min_samples_leaf": sp_randint(1, 11),
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}
In [118]:
# run randomized search
samples = 10  # number of random samples 
randomCV = RandomizedSearchCV(clf, param_distributions = param_dist, n_iter = samples) # default cv = 3
randomCV.fit(x, y)
Out[118]:
RandomizedSearchCV(estimator=RandomForestClassifier(n_estimators=50),
                   param_distributions={'bootstrap': [True, False],
                                        'criterion': ['gini', 'entropy'],
                                        'max_depth': [3, None],
                                        'max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001E2F8C660B0>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001E2F8B33BE0>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001E2F8B309A0>})
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RandomizedSearchCV(estimator=RandomForestClassifier(n_estimators=50),
                   param_distributions={'bootstrap': [True, False],
                                        'criterion': ['gini', 'entropy'],
                                        'max_depth': [3, None],
                                        'max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001E2F8C660B0>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001E2F8B33BE0>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001E2F8B309A0>})
RandomForestClassifier(n_estimators=50)
RandomForestClassifier(n_estimators=50)
In [119]:
randomCV.best_params_
Out[119]:
{'bootstrap': True,
 'criterion': 'gini',
 'max_depth': 3,
 'max_features': 9,
 'min_samples_leaf': 3,
 'min_samples_split': 5}
In [120]:
randomCV.cv_results_['mean_test_score']
Out[120]:
array([0.93554262, 0.93554262, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93617956])
In [121]:
randomCV.best_estimator_
Out[121]:
RandomForestClassifier(max_depth=3, max_features=9, min_samples_leaf=3,
                       min_samples_split=5, n_estimators=50)
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RandomForestClassifier(max_depth=3, max_features=9, min_samples_leaf=3,
                       min_samples_split=5, n_estimators=50)
In [ ]:
In [122]:
# use a full grid over all parameters
param_grid = {"max_depth": [3, None],
              "max_features": [1, 3, 10],
              "min_samples_split": [2, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}
In [123]:
# run grid search
grid_search = GridSearchCV(clf, param_grid = param_grid)
grid_search.fit(x, y)
Out[123]:
GridSearchCV(estimator=RandomForestClassifier(n_estimators=50),
             param_grid={'bootstrap': [True, False],
                         'criterion': ['gini', 'entropy'],
                         'max_depth': [3, None], 'max_features': [1, 3, 10],
                         'min_samples_leaf': [1, 3, 10],
                         'min_samples_split': [2, 3, 10]})
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GridSearchCV(estimator=RandomForestClassifier(n_estimators=50),
             param_grid={'bootstrap': [True, False],
                         'criterion': ['gini', 'entropy'],
                         'max_depth': [3, None], 'max_features': [1, 3, 10],
                         'min_samples_leaf': [1, 3, 10],
                         'min_samples_split': [2, 3, 10]})
RandomForestClassifier(n_estimators=50)
RandomForestClassifier(n_estimators=50)
In [124]:
grid_search.best_params_
Out[124]:
{'bootstrap': True,
 'criterion': 'gini',
 'max_depth': None,
 'max_features': 10,
 'min_samples_leaf': 1,
 'min_samples_split': 2}
In [125]:
grid_search.cv_results_['mean_test_score']
Out[125]:
array([0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93936631, 0.93554262,
       0.93363179, 0.93554262, 0.93872734, 0.93363179, 0.93363179,
       0.93617956, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93617956, 0.93872734, 0.93809039, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       1.        , 1.        , 1.        , 1.        , 0.99936102,
       0.99872204, 0.94828962, 0.95087605, 0.94767099, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93554262, 0.93490568,
       0.93554262, 0.93363179, 0.93490568, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93490568, 0.93426874, 0.93426874, 0.93490568, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.97766224,
       0.9789463 , 0.97892595, 0.96550945, 0.95148247, 0.9553143 ,
       0.94637065, 0.93490771, 0.94129546, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93872734, 0.93872734, 0.93617956, 0.93936428,
       0.93745345, 0.93745345, 0.93363179, 0.93363179, 0.93363179,
       0.93426874, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.94191205,
       0.94191205, 0.93872937, 0.93299485, 0.93490568, 0.93426874,
       0.93363179, 0.93363179, 0.93363179, 1.        , 0.99936306,
       0.98917197, 0.99936102, 0.99744409, 0.99872611, 0.95913596,
       0.96871655, 0.97062534, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93681651, 0.93745345, 0.93363179, 0.93426874, 0.93617956,
       0.93745345, 0.93363179, 0.93426874, 0.93426874, 0.93426874,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93363179,
       0.93363179, 0.93363179, 0.93363179, 0.93363179, 0.93681651,
       0.93426874, 0.93363179, 0.93426874, 0.93617956, 0.93363179,
       0.93363179, 0.93363179, 0.97700494, 0.98530555, 0.9859547 ,
       0.96996805, 0.96550335, 0.96550131, 0.95276449, 0.95977086,
       0.95275025])
In [126]:
grid_search.best_estimator_
Out[126]:
RandomForestClassifier(max_features=10, n_estimators=50)
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RandomForestClassifier(max_features=10, n_estimators=50)
In [ ]:
In [127]:
# fit scaler on training data
norm = MinMaxScaler().fit(x_train)

# transform training data
x_train_norm = norm.transform(x_train)

# transform testing dataabs
x_test_norm = norm.transform(x_test)
In [128]:
# copy of datasets
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()

# apply standardization on numerical features
for i in df_signal_data.select_dtypes([np.number]).columns:
    # fit on training data column
    scale = StandardScaler().fit(x_train_stand[[i]])
    
    # transform the training data column
    x_train_stand[i] = scale.transform(x_train_stand[[i]])
    
    # transform the testing data column
    x_test_stand[i] = scale.transform(x_test_stand[[i]])
In [ ]:
In [129]:
# Comparing Unscaled, Normalized, and Standardized Data
 
knn = KNeighborsRegressor(n_neighbors = 7)

rmse = []

# raw, normalized and standardized training and testing data
trainx = [x_train, x_train_norm, x_train_stand]
testx = [x_test, x_test_norm, x_test_stand]

# model fitting and measuring RMSE
for i in range(len(trainx)):
    knn.fit(trainx[i], y_train)
    pred = knn.predict(testx[i])
    rmse.append(np.sqrt(mean_squared_error(y_test, pred)))

# visualizing the result
df_knn = pd.DataFrame({'RMSE': rmse}, index = ['Original','Normalized','Standardized'])
df_knn
Out[129]:
RMSE
Original 0.523630
Normalized 0.146599
Standardized 0.366971
In [ ]:
In [130]:
# Comparing Unscaled, Normalized, and Standardized Data

svr = SVR(kernel = 'rbf', C = 5)

rmse = []

# raw, normalized and standardized training and testing data
trainx = [x_train, x_train_norm, x_train_stand]
testx = [x_test, x_test_norm, x_test_stand]

# model fitting and measuring RMSE
for i in range(len(trainx)):
    svr.fit(trainx[i], y_train)
    pred = svr.predict(testx[i])
    rmse.append(np.sqrt(mean_squared_error(y_test, pred)))

# visualizing the result    
df_svr = pd.DataFrame({'RMSE': rmse}, index = ['Original','Normalized','Standardized'])
df_svr
Out[130]:
RMSE
Original 0.504901
Normalized 0.082879
Standardized 0.198274
In [ ]:
In [131]:
# Comparing Unscaled, Normalized, and Standardized Data

dt = DecisionTreeRegressor(max_depth = 10, random_state = 27)

rmse = []

# raw, normalized and standardized training and testing data
trainx = [x_train, x_train_norm, x_train_stand]
testx = [x_test, x_test_norm, x_test_stand]

# model fitting and measuring RMSE
for i in range(len(trainx)):
    dt.fit(trainx[i], y_train)
    pred = dt.predict(testx[i])
    rmse.append(np.sqrt(mean_squared_error(y_test, pred)))

# visualizing the result    
df_dt = pd.DataFrame({'RMSE': rmse}, index = ['Original','Normalized','Standardized'])
df_dt
Out[131]:
RMSE
Original 0.0
Normalized 0.0
Standardized 0.0
In [ ]:
In [132]:
# Split x and y into training and test set in 70:30 ratio
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, random_state = 7)
In [133]:
# it takes a list of tuples as parameter. The last entry is the call to the modelling algorithm
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression())
])
In [134]:
# use the pipeline object as you would a regular classifier
pipeline.fit(x_train, y_train)
Out[134]:
Pipeline(steps=[('scaler', StandardScaler()), ('clf', LogisticRegression())])
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Pipeline(steps=[('scaler', StandardScaler()), ('clf', LogisticRegression())])
StandardScaler()
LogisticRegression()
In [135]:
y_predict = pipeline.predict(x_test)
model_score = pipeline.score(x_test, y_test)
In [136]:
print(model_score)

1.0
In [137]:
print(metrics.confusion_matrix(y_test, y_predict))

[[448   0]
 [  0  23]]

E. Display and explain the classification report in detail. [3 Marks]

In [138]:
# Gaussian Naive Bayes on Normal Dataset

nb = GaussianNB()
nb.fit(x_train, y_train)
Out[138]:
GaussianNB()
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GaussianNB()
In [139]:
modelnb_score = nb.score(x_train, y_train)
print('Accuracy Score of Training Data: ', modelnb_score)

Accuracy Score of Training Data:  0.9936131386861314
In [140]:
y_predictnb= nb.predict(x_test)
modelnb_score = accuracy_score(y_test, y_predictnb)
print('Accuracy Score of Test Data:', modelnb_score)

Accuracy Score of Test Data: 0.9893842887473461
In [141]:
#printing classification report
print("Classification Report")
print(metrics.classification_report(y_test, y_predictnb, labels=[1, 0]))

Classification Report
              precision    recall  f1-score   support

           1       0.82      1.00      0.90        23
           0       0.00      0.00      0.00         0

   micro avg       0.82      1.00      0.90        23
   macro avg       0.41      0.50      0.45        23
weighted avg       0.82      1.00      0.90        23


C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
In [142]:
# visualizing confusion matrix
cm= confusion_matrix(y_test, y_predictnb)
plt.figure(figsize = (6, 4))
sns.heatmap(cm, annot = True, cmap = 'RdYlGn', fmt = 'd')
plt.xlabel('Predicted Classes', fontsize = 15)
plt.ylabel('Actual Classes', fontsize = 15)
plt.title('Confusion Matrix for GNB', fontsize = 15);
In [143]:
precision_nb, recall_nb, f1_score_nb, support = precision_recall_fscore_support(y_test, y_predictnb, average = 'macro')
print('Precision Score :', '%0.2f' % precision_nb)
print('Recall Score :', '%0.2f' % recall_nb)
print('F1-Score:', '%0.2f' % f1_score_nb)
nb_acc= accuracy_score(y_test, y_predictnb)
print('Accuracy Score :','%0.2f' % nb_acc)
print('Thresholdnb :','%0.2f' % 0.016)
Thresholdnb=0.016

Precision Score : 0.91
Recall Score : 0.99
F1-Score: 0.95
Accuracy Score : 0.99
Thresholdnb : 0.02
In [ ]:
In [144]:
# Gaussian Naive Bayes on Under sampled Data

nbu = GaussianNB()
nbu.fit(x_under, y_under)
Out[144]:
GaussianNB()
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GaussianNB()
In [145]:
modelnbu_score = nbu.score(x_under,y_under)
print('Accuracy Score of Training Data: ', modelnbu_score)

Accuracy Score of Training Data:  1.0
In [146]:
y_predictnbu= nbu.predict(x_test)
modelnbu_score = accuracy_score(y_test, y_predictnbu)
print('Accuracy Score of Test Data:', modelnbu_score)

Accuracy Score of Test Data: 0.9766454352441614
In [147]:
#printing classification report
print("Classification Report")
print(metrics.classification_report(y_test, y_predictnbu, labels=[1, 0]))

Classification Report
              precision    recall  f1-score   support

           1       0.68      1.00      0.81        23
           0       0.00      0.00      0.00         0

   micro avg       0.68      1.00      0.81        23
   macro avg       0.34      0.50      0.40        23
weighted avg       0.68      1.00      0.81        23


C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
In [148]:
# visualizing confusion matrix
cm= confusion_matrix(y_test, y_predictnbu)
plt.figure(figsize = (6, 4))
sns.heatmap(cm, annot = True, cmap = 'RdYlGn', fmt = 'd')
plt.ylabel('Actual Classes', fontsize = 15)
plt.xlabel('Predicted Classes', fontsize = 15)
plt.title('Confusion Matrix for GNB Under sampled', fontsize = 15);
In [149]:
precision_nbu, recall_nbu, f1_score_nbu, support = precision_recall_fscore_support(y_test, y_predictnbu, average = 'macro')
print('Precision Score :', '%0.2f' % precision_nbu)
print('Recall Score :', '%0.2f' % recall_nbu)
print('F1-Score:', '%0.2f' % f1_score_nbu)
nbu_acc= accuracy_score(y_test, y_predictnbu)
print('Accuracy Score :','%0.2f' % nbu_acc)
print('Thresholdnbu:','%0.2f' % 0.4753)
Thresholdnbu=0.4753

Precision Score : 0.84
Recall Score : 0.99
F1-Score: 0.90
Accuracy Score : 0.98
Thresholdnbu: 0.48
In [ ]:
In [150]:
n_estimators = [int(x) for x in np.linspace(start = 50, stop = 500, num = 10)]
random_grid = {'n_estimators': n_estimators, 'criterion':['gini','entropy']}
In [151]:
# RandomForest on Random over sampled Dataset

rf = RandomForestClassifier()
rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, cv = 5, verbose=2, random_state=90, n_jobs = -1)
rf_random.fit(x_over, y_over)

Fitting 5 folds for each of 10 candidates, totalling 50 fits
Out[151]:
RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,
                   param_distributions={'criterion': ['gini', 'entropy'],
                                        'n_estimators': [50, 100, 150, 200, 250,
                                                         300, 350, 400, 450,
                                                         500]},
                   random_state=90, verbose=2)
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RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,
                   param_distributions={'criterion': ['gini', 'entropy'],
                                        'n_estimators': [50, 100, 150, 200, 250,
                                                         300, 350, 400, 450,
                                                         500]},
                   random_state=90, verbose=2)
RandomForestClassifier()
RandomForestClassifier()
In [152]:
modelrfg1_score=rf_random.score(x_over,y_over)
print('Accuracy Score of Training Data: ', modelrfg1_score)

Accuracy Score of Training Data:  1.0
In [153]:
y_predictrfg1= rf_random.predict(x_test)
modelrfg1_score = accuracy_score(y_test, y_predictrfg1)
print('Accuracy Score of Test Data:', modelrfg1_score)

Accuracy Score of Test Data: 0.9957537154989384
In [154]:
#printing classification report
print("Classification Report")
print(metrics.classification_report(y_test, y_predictrfg1, labels=[1, 0]))

Classification Report
              precision    recall  f1-score   support

           1       1.00      0.91      0.95        23
           0       0.00      0.00      0.00         0

   micro avg       1.00      0.91      0.95        23
   macro avg       0.50      0.46      0.48        23
weighted avg       1.00      0.91      0.95        23


C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Bhavya Govindrao\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
In [155]:
# visualizing confusion matrix
cm= confusion_matrix(y_test, y_predictrfg1)
plt.figure(figsize = (6, 4))
sns.heatmap(cm, annot = True, cmap = 'RdYlGn', fmt = 'd')
plt.ylabel('Actual Classes', fontsize = 15)
plt.xlabel('Predicted Classes', fontsize = 15)
plt.title('Confusion Matrix for RF Over Sampled', fontsize = 15);
In [156]:
precision_rfo, recall_rfo, f1_score_rfo, support = precision_recall_fscore_support(y_test, y_predictrfg1, average = 'macro')
print('Precision Score :', '%0.2f' % precision_rfo)
print('Recall Score :', '%0.2f' % recall_rfo)
print('F1-Score:', '%0.2f' % f1_score_rfo)
rfo_acc= accuracy_score(y_test, y_predictrfg1)
print('Accuracy Score :','%0.2f' % rfo_acc)
print('Thresholdrf :','%0.2f' % 0.1688)
Thresholdrf=0.1688

Precision Score : 1.00
Recall Score : 0.96
F1-Score: 0.98
Accuracy Score : 1.00
Thresholdrf : 0.17

F. Apply the above steps for all possible models that you have learnt so far. [5 Marks]

In [157]:
lrcl = LogisticRegression()
nbcl = GaussianNB()
dtcl = DecisionTreeClassifier()
lr = LinearRegression()
knncl = KNeighborsClassifier()
svcl= SVC()
svr = SVR()
rfcl = RandomForestClassifier()
bgcl = BaggingClassifier()
nncl = MLPClassifier()
In [158]:
for clf, label in zip([lrcl , nbcl, dtcl, lr, knncl, svcl, svr,  rfcl, bgcl, nncl], 
    ['lrcl' , 'nbcl', 'dtcl', 'lr', 'knncl', 'svcl', 'svr',  'rfcl',  'bgcl', 'nncl']):
    print("model name: " , label)
    print("\n model_hyperparameters \n" , clf.get_params())

model name:  lrcl

 model_hyperparameters 
 {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}
model name:  nbcl

 model_hyperparameters 
 {'priors': None, 'var_smoothing': 1e-09}
model name:  dtcl

 model_hyperparameters 
 {'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}
model name:  lr

 model_hyperparameters 
 {'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}
model name:  knncl

 model_hyperparameters 
 {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}
model name:  svcl

 model_hyperparameters 
 {'C': 1.0, 'break_ties': False, 'cache_size': 200, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'rbf', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}
model name:  svr

 model_hyperparameters 
 {'C': 1.0, 'cache_size': 200, 'coef0': 0.0, 'degree': 3, 'epsilon': 0.1, 'gamma': 'scale', 'kernel': 'rbf', 'max_iter': -1, 'shrinking': True, 'tol': 0.001, 'verbose': False}
model name:  rfcl

 model_hyperparameters 
 {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}
model name:  bgcl

 model_hyperparameters 
 {'base_estimator': 'deprecated', 'bootstrap': True, 'bootstrap_features': False, 'estimator': None, 'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 10, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}
model name:  nncl

 model_hyperparameters 
 {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 200, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}
  1. Post Training and Conclusion: [5 Marks]

A. Display and compare all the models designed with their train and test accuracies. [1 Marks]

B. Select the final best trained model along with your detailed comments for selecting this model. [1 Marks]

In [159]:
modellists = []
modellists.append(['Gaussian NB Normal Data', nb_acc * 100, recall_nb * 100, precision_nb * 100,f1_score_nb*100,Thresholdnb])
modellists.append(['Gausian NB under samples data', nbu_acc* 100, recall_nbu * 100, precision_nbu* 100,f1_score_nbu*100,Thresholdnbu])
modellists.append(['Random Forest Over sampled Data', rfo_acc * 100, recall_rfo * 100, precision_rfo * 100,f1_score_rfo*100,Thresholdrf])
model_df = pd.DataFrame(modellists, columns = ['Model', 'Accuracy Scores on Test', 'Recall Score', 'Precision Score','F1 Score','Threshold'])
model_df
Out[159]:
Model Accuracy Scores on Test Recall Score Precision Score F1 Score Threshold
0 Gaussian NB Normal Data 98.938429 99.441964 91.071429 94.817456 0.0160
1 Gausian NB under samples data 97.664544 98.772321 83.823529 89.729408 0.4753
2 Random Forest Over sampled Data 99.575372 95.652174 99.777778 97.615914 0.1688

C. Pickle the selected model for future use. [2 Marks]

In [160]:
import pickle

regressor = LinearRegression()
#Fitting model with training data
regressor.fit(x, y)
Out[160]:
LinearRegression()
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LinearRegression()
In [161]:
pickle.dump(regressor, open('model.pkl','wb'))
In [162]:
# Loading model to compare the results
model = pickle.load(open('model.pkl','rb'))
In [163]:
model
Out[163]:
LinearRegression()
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LinearRegression()

D. Write your conclusion on the results. [1 Marks]

In [ ]: